Strategic Risk Management and Product Market Competition - PowerPoint PPT Presentation

Loading...

PPT – Strategic Risk Management and Product Market Competition PowerPoint presentation | free to download - id: 71e5ff-YzVhY



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Strategic Risk Management and Product Market Competition

Description:

Most variation in derivatives strategies cannot be explained by traditional ... The ADT Model Analyze firms hedging decisions within the context of an ... – PowerPoint PPT presentation

Number of Views:14
Avg rating:3.0/5.0
Slides: 29
Provided by: TimA164
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Strategic Risk Management and Product Market Competition


1
Strategic Risk Management and Product Market
Competition
  • Tim R. Adam
  • National University of Singapore RMI
  • Amrita Nain
  • McGill University
  • Comments welcome!

2
Theory of Corporate Risk Management
  • Firm-specific factors
  • Taxes (Smith and Stulz, 1985)
  • Financial distress costs (Smith and Stulz, 1985)
  • Information asymmetries agency costs (Froot,
    Scharfstein and Stein, 1993, DeMarzo and Duffie,
    1991, )
  • Risk-aversion of stakeholders (Smith and Stulz,
    1985)
  • Industry-specific factors
  • Degree of competition, hedging decisions of
    competitors (Mello Ruckes, 2006, Adam, Dasgupta
    and Titman, 2007)
  • Derivatives decisions are not made in isolation
    but take the decisions of competitors into
    account.

3
Empirical Literature
  • Nance, Smith and Smithson (1993), Mian (1996),
    Dolde (1993) Geczy, Minton and Schrand (1997),
    Tufano (1996), Haushalter (2000), Allayannis and
    Ofek (2001), Brown (2001), Graham and Rogers
    (2002), Adam and Fernando (2006), Lel (2006),
  • Most variation in derivatives strategies cannot
    be explained by traditional models of hedging /
    firm-specific factors.
  • Brown (2001) studies risk management at a major
    durable goods producer (HDG).
  • Earnings management and competitive concerns in
    the product market motivate HDGs FX risk
    management rather than the traditional models of
    hedging.
  • HDG tracks the hedging programs of its major
    US-based competitors.

4
Objective
  • Are industry-specific factors likely to be
    important in determining a firms derivatives
    strategy?
  • Do the derivatives strategies of competitors
    matter?
  • Does the degree of competition affect derivatives
    strategies?
  • Derive testable hypotheses based on the models by
    Adam, Dasgupta and Titman (2007), and Mello and
    Ruckes (2006).

5
The ADT Model
  • Analyze firms hedging decisions within the
    context of an industry equilibrium.
  • n identical firms, Cournot competition
  • Common cash flow (cost) shocks
  • Firms hedge their cash flows as in FSS (1993)
  • Cost effect Hedging reduces expected costs
  • Flexibility (real option) effect Volatility in
    cash flows is beneficial because firms can choose
    output after observing their cash flows.
  • Low cash flow ? high marginal cost ? reduce
    production
  • High cash flow ? low marginal cost ? increase
    production
  • Shleifer and Vishney (1992) effect Firms benefit
    if their cash flows are high when their
    competitors have low cash flows and vice versa.
  • Low agg. cash flow ? high price ? high investment
    opportunities
  • High agg. cash flow ? low price ? low investment
    opportunities

6
Why Symmetric Equilibria Dont Exist
  • Suppose all firms hedge their cash flows
  • Constant cash flows ? constant costs ? constant
    output ? constant price
  • A financially constrained firm benefits from
    volatility in its cash flow (marginal cost)
    because when its cash flow is high it produces
    more and when its cash flow is low it produces
    less. (Flexibility effect)
  • Suppose no firm hedges
  • Variable cash flows ? variable costs ? variable
    output ? variable price
  • Firms have high cash flows when prices are low
    and vice versa.
  • A financially constrained firm benefits from
    shifting cash from states with low marginal
    productivity (high cash flow states) to those
    with high marginal productivity (low cash flow
    states). (Shleifer and Vishney (1992) effect)

7
Testable Hypotheses
  • Do derivatives strategies of competitors matter?
  • Is the sensitivity of output prices (to FX
    shocks) affected by aggregate hedging decisions?
  • Is a firms exposure affected by aggregate
    hedging decisions?
  • Most firms hedge
  • Exposure of a hedged firm is low
  • Exposure of an unhedged firm is high
  • Most firms do not hedge
  • Exposure of an unhedged firm is low
  • Exposure of a hedged firm is high
  • Degree of competition
  • Does the degree of competition affect aggregate
    hedging decisions?

8
Data
  • Derivatives data
  • Search all SEC 10-K filings for year of 1999 for
    text strings such as hedg, swap, cap,
    forward, etc.
  • Match sample with Compustat firms. Exclude
    financial firms and utilities.
  • Collect gross notional amounts of FX derivatives
    (forwards, swaps, options).
  • Ex-ante exposure data
  • We classify firms as having ex-ante FX exposure
    if they disclose foreign assets, foreign sales,
    foreign income, foreign taxes, exchange rate
    effect, or foreign currency adjustments.

FX exposure No FX exposure
FCD user 429 119 548
FCD non-user 2,377 3,461 5,838
2,806 3,580 6,386
9
Firm Characteristics
Mean Med. Std. dev Min Max Obs
Market value of assets (in millions of US) 4,302 347.7 18,736 0.076 408,030 2,398
Tobins q 2.129 1.475 1.906 0.525 19.51 2,387
Debt-equity ratio 0.565 0.146 1.398 0 22.09 2,293
Quick ratio 1.820 1.283 1.688 0.053 16.54 2,713
Payout ratio 0.130 0 0.618 0 15 2,719
Foreign sales / net sales 0.357 0.293 0.273 0.000 1 2,398
FCD users (dummy variable) 0.153 0 0.360 0 1 2,806
Notional value of FX derivatives / market value of assets 0.079 0.028 0.213 0.000 2.96 417
Firms with ex-ante FX exposure only.
10
Industry Characteristics (6-digit NAICS)
Mean Med. Std. dev Min Max Obs
Number of public firms 9.5 3 1 802
Weighted fraction of exposed firms 0.576 0.797 0.433 0 1 766
Median exposure (exposed firms) 0.318 0.242 0.258 0.000 1 526
Market value weighted fraction of FCD users (exposed firms) 0.195 0 0.324 0 1 802
Industry weighted average hedge ratio (exposed firms) 0.010 0 0.041 0 0.481 787
11
Estimating the Sensitivity of Producer Prices to
FX shocks
  • Following Feinberg (1989), we estimate the
    following model using monthly data from 1996 to
    2000.
  • RPPIjt real producer price index
  • EXCHt real trade-weighted value of the U.S.
    dollar against its major trading partners
  • FRACTIONjt market value-weighted fraction of
    FCD users
  • Price sensitivity may be a function of FRACTION
    (endog.) Instrument fraction of IR derivatives
    users (2SLS) model is estimated in log changes
    Newey-West standard errors.

12
Price Sensitivity to FX Shocks
Dependent variable ?ln RPPIt1 Dependent variable ?ln RPPIt1
?ln EXCHt -0.076 -0.077
?ln EXCHt Fraction of FCD users 0.433 0.436
?ln EXCHt Foreign inputs -0.964
?ln EXCHt Exports 4.565 5.949
?ln EXCHt Industry concentration -2.253 -2.180
?ln EXCHt Foreign competition -1.457
?ln EXCHt Capital intensity 1.154 0.994
Industry dummies controls Yes Yes
Observations 5,211 5,211
F-statistic 3.46 3.55
13
Key Results
  • When the USD depreciates (EXCH ?) and the cost of
    imports rise, domestic producer prices increase.
  • A real depreciation of the US by 10 increases
    real domestic producer prices by 0.77.
  • The price sensitivity (pass-through) is lower
  • in industries in which FX derivatives usage is
    more widespread
  • in industries that use fewer foreign inputs
  • in industries that export more
  • in less concentrated (more competitive)
    industries

14
Determinants of Exposure
  • Is a firms exposure affected by aggregate
    hedging decisions?
  • Estimate firms ex-post FX exposures.
  • Analyze the exposures of FCD users and non-users.

Fraction of FCD users - high FCD user low exposure
Fraction of FCD users - high FCD non-user high exposure
Fraction of FCD users - low FCD user high exposure
Fraction of FCD users - low FCD non-user low exposure
15
Estimating the FX Exposure of Firms
  • For each firm we estimate the following market
    model using monthly returns from 1996 to 2000.
  • rit firm is stock return
  • rmt value-weighted market return
  • ?EXCHt change in trade-weighted value of the
    U.S. dollar against its major trading partners
  • The FX exposure estimates ßix range from -1.03 to
    1.22. Out of 3,036 firms 344 firms have
    significant exposures to the trade-weighted value
    of the U.S. dollar.

16
Comparison of FX Exposures
All firms FCD users FCD non-users Difference between users and non-users
abs(FX ex-posure) ßix 0.010 0.001 0.020 0.004 -0.010
FX exposure if ßix gt 0 0.012 0.011 0.008 0.020 0.013 -0.018
FX exposure if ßix lt 0 -0.009 -0.010 -0.007 -0.016 -0.009 0.005
Top figures denote means, bottom figures denote
medians.
FCD users have lower exposures to the
trade-weighted value of the U.S. dollar than FCD
non-users.
17
Distribution of Exposure Coefficients
Avg. FRACTION of FCD Users 0.42
Avg. FRACTION of FCD Users 0.35
Avg. FRACTION of FCD Users 0.33
Avg. FRACTION of FCD Users 0.39
18
Aggregate Hedging and FX Exposures
Dependent variable ßix Dependent variable ßix
Intercept 1.535 1.240
FCD user -0.122
FCD user FRACTION -0.990
FRACTION 0.816
FCD non-user 1.111
FCD non-user (1-FRACTION) -0.990
(1-FRACTION) 0.174
Control variables Yes Yes
Observations 2826 2826
F-statistic 10.83 10.83
19
Aggregate Hedging and FX Exposures
Dependent variable ßix Dependent variable ßix
FCD user -0.192
FCD user FRACTION -0.966
FRACTION 0.799
FCD user Pass-through coefficient -3.528
Pass-through coefficient 0.747
FCD non-user 4.686
FCD non-user (1-FRACTION) -0.966
1-FRACTION 0.167
FCD non-user (1-Pass-through coeff.) -3.528
(1-Pass-through coefficient) 2.782
Observations 2826 2826
F-statistic 12.67 12.67
20
Key Results
  • FCD users have lower ex-post FX exposures than
    FCD non-users.
  • As the fraction of derivatives users increases,
    the exposure
  • of FCD users declines
  • of FCD non-users increases.

FCD user FCD non-user
Fraction of FCD users - high low exposure high exposure
Fraction of FCD users - low high exposure low exposure
21
Derivatives Usage and Competition
  • Allayannis and Ihrig (2001)
  • Exposures increase as mark-ups fall.
  • ? Firms that operate in more competitive
    industries face larger exposures and therefore
    are more likely to hedge.
  • Mello and Ruckes (2006)
  • Firms hedge less if competition is more intense
    in order to gain a competitive advantage (market
    share) if prices move favorably.
  • Adam, Dasgupta and Titman (2007)
  • Competition can have a positive or negative
    impact on the number of firms that hedge in
    equilibrium, depending on whether hedging or not
    hedging is optimal in the absence of any
    competitive interaction between firms.

22
Testable Hypotheses
Fraction of FCD users
½
of firms (competition)
  • Degree of competition
  • Does the degree of competition affect aggregate
    hedging decisions?
  • Do firms hedge less in more competitive
    industries?

23
Equilibrium
In equilibrium E?h(w) E?u(w) ? 0 The
proportion of firms that use derivatives is given
by
  • Flexibility effect dominates
  • cost reduction effect
  • Small market share (a - a)
  • Cost reduction dominates
  • flexibility effect
  • Large market share (a - a)

Fraction of firms hedging
0
½
1
24
Measuring the Degree of Competition
Mean Median Std.dev Min Max Obs.
PCM 0.324 0.305 0.163 0 1 701
PCMCensus 0.337 0.329 0.099 0.094 0.818 350
Herfindahl indexCensus 0.423 0.394 0.265 0.009 0.999 237
Concentration ratio (top 4 firms) 0.423 0.406 0.209 0.036 1 349
Concentration ratio (top 8 firms) 0.553 0.561 0.223 0.066 1 346
Herfindahl indexCensus Herfindahl indexCensus
PCMCensus Below median Above median Total
Below median 74 54 128
Above median 45 64 109
Total 119 118 237
25
Fraction of FCD Users
Intercept -0.703 (-5.73) -0.671 (-4.07) -0.385 (-1.89) -0.480 (-3.28) -0.545 (-2.60) -0.565 (-3.81)
PCM 0.665 (3.55)
PCMCensus 1.296 (3.54)
Herfindahl indexCensus 0.362 (2.01)
Concentration ratio (top 4 firms) 0.483 (2.67)
PCMCensus ? Herfindahl index 0.814 (3.66)
PCMCensus ? Concentration ratio 0.661 (4.07)
Weighted fraction of exposed firms 0.495 (6.50) 0.260 (2.55) 0.367 (2.75) 0.306 (2.96) 0.324 (2.46) 0.279 (2.74)
ln(median firm size) 0.050 (2.80) 0.049 (2.39) 0.019 (0.60) 0.029 (1.31) 0.019 (0.63) 0.034 (1.62)
Median Tobins q -0.128 (-2.95) -0.086 (-1.23) -0.077 (-0.87) -0.005 (-0.07) -0.127 (-1.41) -0.051 (-0.76)
Number of obs. 659 338 231 337 231 337
Pseudo R2 0.086 0.057 0.041 0.047 0.067 0.065
26
Intercept -0.506 (-2.44) -0.650 (-2.89) -0.335 (-1.36) -0.360 (-1.80) -0.525 (-2.01) -0.476 (-2.31)
PCM 0.679 (2.45)
PCMCensus 1.083 (2.84)
Herfindahl indexCensus 0.102 (0.50)
Concentration ratio (top 4 firms) 0.066 (0.27)
PCMCensus ? Herfindahl index 0.571 (2.32)
PCMCensus ? Concentration ratio 0.433 (2.20)
Weighted fraction of exposed firms 0.415 (3.24) 0.374 (2.67) 0.574 (3.56) 0.441 (3.12) 0.508 (3.18) 0.391 (2.77)
ln(median firm size) 0.001 (0.04) 0.010 (0.26) 0.024 (0.50) 0.004 (0.11) 0.021 (0.45) 0.003 (0.08)
Price sensitivity 0.502 (1.29) 0.962 (1.91) 1.273 (1.78) 0.809 (1.59) 1.099 (1.57) 0.837 (1.66)
Cost convexity 0.459 (1.76) 0.272 (0.89) -0.102 (-0.23) 0.252 (0.80) -0.117 (-0.26) 0.213 (0.69)
ln(market share) 0.025 (0.59) 0.028 (0.59) -0.011 (-0.17) 0.034 (0.68) -0.011 (-0.18) 0.027 (0.55)
Fraction of firms with investment grade rating -0.192 (-0.59) -0.373 (-1.08) -0.308 (-0.52) -0.217 (-0.63) -0.328 (-0.57) -0.272 (-0.79)
Number of obs. 212 183 132 183 132 183
Pseudo R2 0.090 0.093 0.092 0.067 0.115 0.083
27
Extent of FCD Usage
Intercept -0.197 (-8.96) -0.193 (-6.24) -0.167 (-5.44) -0.184 (-6.71) -0.182 (-5.57) -0.194 (-6.79)
PCM -0.015 (-0.54)
PCMCensus 0.054 (1.06)
Herfindahl indexCensus 0.024 (1.09)
Concentration ratio (top 4 firms) 0.032 (1.11)
PCMCensus ? Herfindahl index 0.053 (1.95)
PCMCensus ? Concentration ratio 0.045 (1.84)
Fraction of exposed firms 0.126 (8.41) 0.127 (5.51) 0.113 (4.58) 0.130 (5.61) 0.109 (4.46) 0.127 (5.53)
ln(Median firm size) 0.012 (4.40) 0.007 (2.28) 0.006 (1.66) 0.006 (1.75) 0.007 (1.74) 0.006 (1.91)
Number of obs. 663 340 232 339 232 339
28
Summary
  • Output prices are less sensitive to FX shocks
    (lower pass-through) if more firms use
    derivatives.
  • Firms FX exposures appear to be a function of
    the prevalence of derivatives usage.
  • If derivatives usage is widespread, FCD users
    exhibit relatively low exposures, while FCD
    non-users exhibit relatively high exposures.
  • If derivatives usage is less common, FCD users
    exhibit relatively high exposures, while FCD
    non-users exhibit relatively low exposures.
  • In more competitive industries fewer firms use
    derivatives.
  • In more competitive industries the average size
    of derivatives positions is lower.
About PowerShow.com